To simplify visualization of various GenomeRunner results, a wrapper function has been created - mtx.sig <- showHeatmap. See the utils.R file for more details.

The mtx.sig <- showHeatmap function returns filtered and clustered matrixes of the -log10-transformed p-values.

Regulatory similarity visualization

Input: an N x M enrichment matrix, where N are the regulatory datasets, M are the sets of features of interest (FOIs, e.g., SNP sets), and each cell represent the corresponding enrichment p-value.

Output: a heatmap of Pearson or Spearman correlation coefficients among the sets of FOIs (columns), and a numerical matrix of correlation coefficients used for the heatmap. The numerical matrix can be saved into a file.

mtx.sig <- showHeatmap(paste(dirs[2], "matrix.txt", sep = "/"), colnum = seq(1, 50), factor = "none", cell = "none", isLog10 = FALSE, adjust = "none", pval = 0.5, numtofilt = 1, toPlot = "corrSpearman")

Cell type-specific visualization

There are two particularly interesting categories of regulatory datasets provided by the ENCODE project:

Histone - 722 cell-type specific histone modification marks. Information about individual marks can be looked up on Histome: the Histone Infobase.
TFBSs - 1952 cell-type specific transcription factor binding sites. Information about individual transcription factors can be looked up on Factorbook.

Information about the cell lines used in the ENCODE project can be found at the ENCODE cell types portal.

Input: an enrichment matrix. Specify which column and which category to use for visualization. Tweak the number of missing values (NAs) allowed in rows/columns - the rows/columns having more NAs will be filtered out.

Output: a heatmap of cell x regulatory mark enrichment results, and a numerical matrix of -log10-transformed p-valued used for the heatmap. The color key shows the range of the -log10-transformed p-values.

The numerical matrix can be saved into a file. The -log10-transformed p-values can be converted to regular p-value scale in Excel using ’=1/POWER(10, ABS(A1))*SIGN(A1)’ formula. Note a “-” sign indicates significant depletion instead of enrichment.

Instead of using all available cell lines, subsets of tissue-specific cell lines can be used. For example:

Brain cell lines: c(“Sknsh”, “Sknshra”, “Nha”, “Pfsk1”, “Sknmc”, “Be2c”, “U87”, “Hah”, “Gliobla”, “M059j”, “Medullo”, “Shsy5y”, “Bcbrainh11058n”, “Medullod341”)
Lung cell lines: c(“A549”, “Imr90”, “Hpae”, “Hpaf”, “Hpf”, “Nhbe”, “Nhbera”, “Nhlf”, “Saec”, “Tbec”, “Wi38”)

The ENCODE datasets are cell-type incomplete, that is, for one cell type the data about the distribution of all histone marks are available, but the other cell type may have only a few histone mark datasets. So, the matrixes of cells x marks will have many missing values. They have to be filtered to remove rows/columns containing too much missing values, otherwise, clustering and visualization algorithms break. The heatmaps of such filtered matrixes are shown.

Enrichment of the Crohn’s disease-associated SNPs in histone marks in different cell types

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = 62, factor = "Histone", cell = "none", isLog10 = FALSE, adjust = "none", pval = 0.1, numtofilt = 2, toPlot = "heat")

Enrichment of the Inflammatory bowel disease-associated SNPs in histone marks in different cell types

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = 102, factor = "Histone", cell = "none", isLog10 = FALSE, adjust = "none", pval = 0.1, numtofilt = 4, toPlot = "heat")

Enrichment of the Inflammatory bowel disease-associated SNPs in transcription factor binding sites in different cell types

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = 102, factor = "Tfbs", cell = "none", isLog10 = FALSE, adjust = "none", pval = 1, numtofilt = 7, toPlot = "heat")

Barplot of the enrichments of the Crohn’s disease-associated SNPs in histone marks in Monocd14ro1746 cell line

Each bar represents the -log10-transformed enrichment p-value - the higher the bar the more significant the enrichment is.

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = 102, factor = "Histone", cell = "Monocd14ro1746", isLog10 = FALSE, adjust = "fdr", pval = 0.1, numtofilt = 7, toPlot = "barup")

Comparing the enrichments of the Crohn’s disease and the Inflammatory bowel disease-associated SNPs in histone marks in Gm12878 cell line

One, or several comparisons can be plotted. Note, if two conditions are plotted, the barplot is split in two parts - one part shows the most significant enrichments for the first condition, while the other showls the most significant enrichments for the second condition.

Both overrepresented and underrepsesented barplots can be plotted.

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = c(62, 102), factor = "Histone", cell = "Gm12878", isLog10 = FALSE, adjust = "fdr", pval = 0.1, numtofilt = 7, toPlot = "barup")

Observe enrichment p-values change in among different SNP sets for H3K4me1 and H3K4me2 histone marks, in Gm12878 cell line

showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = c(62, 102, 135, 195), factor = "H3k4me1|H3k4me2", cell = "Gm12878", isLog10 = FALSE, adjust = "none", pval = 0.1, numtofilt = 1, toPlot = "lines")

Investigate regulatory similarity among different SNP sets using enrichment in histone marks in Gm12878 cell line

Regualtory similarity analysis compares SNP set-specific regulatory enrichment profiles using Pearson or Spearman correlation coefficient.

mtx.sig <- showHeatmap(paste(dirs[1], "matrix.txt", sep = "/"), colnum = c(62, 102, 135, 195), factor = "Histone", cell = "Gm12878", isLog10 = FALSE, adjust = "none", pval = 0.1, numtofilt = 1, toPlot = "corrPearson")

Overview of visualization functionality of GenomeRunner

Mikhail Dozmorov